Qualitative Comparison. To analyze the effectiveness of UCN, we compare it with the previous state-of-the-art method MCT [1]} based on the prototype refining strategy.
Figure REF  visualized ProtoNet-style [2]} prototype (support instances), instance feature maps (query instances), refined prototype generated by our method (UC prototype), MCT prototype, and the oracle prototype.
The arrows mark the changes made to the prototype by the MCT and UCN methods.
The oracle prototype is obtained by averaging the features of all samples of the same class in the dataset,
which can represent the commonality of instances in a category because the averaging operation neutralizes the random noise over a wide range of instances.
The RGB value of the points for query instances \(\mathbf {x}_j^Q\)  is determined by the proportion among the weights \(w_{0,j}^{(T)}\) , \(w_{1,j}^{(T)}\)  and \(w_{2,j}^{(T)}\) , and its transparency reflects the maximum weight of the corresponding query instance, that is \(\max (w_{0,j}^{(T)}, w_{1,j}^{(T)}, w_{2,j}^{(T)})\) .
In this way, we can intuitively understand the contribution of each query instance to the refined prototypes.
In addition, Figure REF , REF  also draw the approximate decision boundaries based UC prototypes and MCT prototypes, respectively.
All misclassified query instances are also marked with ground truth labels.
